Dauer:
1 Semester | Angebotsturnus:
Jedes Sommersemester | Leistungspunkte:
4 |
Studiengang, Fachgebiet und Fachsemester: - Master Medizinische Ingenieurwissenschaft 2020 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
- Master Entrepreneurship in digitalen Technologien 2020 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
- Master Medieninformatik 2020 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
- Master Biophysik 2023 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
- Master Psychologie 2016 (Wahlpflicht), Wahlpflicht, Beliebiges Fachsemester
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Lehrveranstaltungen: - CS4575-V: Sequence Learning (Vorlesung, 2 SWS)
- CS4575-Ü: Sequence Learning (Übung, 1 SWS)
| Workload: - 75 Stunden Selbststudium
- 45 Stunden Präsenzstudium
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Lehrinhalte: | - Introduction to Sequence Learning (Formalisms, Metrics, Recapitulation of Relevant Machine Learning Techniques)
- Recurrent Neural Networks (Simple RNN Models, Backpropagation Through Time)
- Gated Recurrent Networks (Vanishing Gradient Problem in RNNs, Long Short-Term Memories, Gated Recurrent Units, Stacked RNNs)
- Important Techniques for RNNs (Teacher Forcing, Scheduled Sampling, h-Detach)
- Bidirectional RNNs and related concepts
- Hierarchical RNNs and Learning on Multiple Time Scales
- Online Learning and Learning without BPTT (Real-Time Recurrent Learning, e-Prop, Forward Propagation Through Time)
- Reservoir Computing (Echo State Networks, Deep ESNs)
- Spiking Neural Networks (Spiking Neuron Models, Learning in SNNs, Neuromorphic Computing, Recurrent SNNs)
- Temporal Convolution Networks (Causal Convolution, Temporal Dilation, TCN-ResNets)
- Introduction to Transformers (Sequence-to-Sequence Learning, Basics on Attention, Self-Attention and the Query-Key-Value Principle, Large Language Models)
- State Space Models (Structured State Space Sequence Models, Mamba)
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Qualifikationsziele/Kompetenzen: - Students get a comprehensive understanding of most relevant sequence learning approaches
- Students learn to analyze the challenges in sequence learning tasks and to identify well-suited approaches to solve them
- Students will understand the pros and cons of various sequence learning models
- Students can implement common and custom sequence learning models for time series analysis, classification, and forecasting
- Students know how to analyze the models and results, to improve the model parameters, and to interpret the model predictions and their relevance
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Vergabe von Leistungspunkten und Benotung durch: - Klausur oder mündliche Prüfung nach Maßgabe des Dozenten
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Modulverantwortlicher: Lehrende: - MitarbeiterInnen des Instituts
- Prof. Dr. Sebastian Otte
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Literatur: - Goodfellow, I., Bengio, Y., & Courville, A. (2016): Deep Learning - MIT Press. ISBN 978-0262035613
- Prince, S. J. D. (2023): Understanding Deep Learning - The MIT Press. ISBN 978-0262048644
- Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020): Mathematics for Machine Learning - Cambridge University Press, 2020. ISBN 978-1108470049
- Nakajima, K., & Fischer, I. (2021): Reservoir Computing: Theory, Physical Implementations, and Applications - Cambridge University Press, 2020. ISBN 978-1108470049
- Sun, R., & Giles, C. (2001): Sequence Learning: Paradigms, Algorithms, and Applications - Springer Berlin Heidelberg. ISBN 978-3540415978
- Bishop, C. M. (2006): Pattern Recognition and Machine Learning - Springer. ISBN 978-0387310732
- Recent publications on the related topics:
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Sprache: - Wird nur auf Englisch angeboten
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Bemerkungen:Admission requirements for taking the module:
- None, but it is recommended to complete the course Deep Learning (CS4295-KP04) first
Admission requirements for participation in module examination(s):
- Successful completion of exercise assignments as specified at the beginning of the semester
Module Exam(s):
- CS4575-L1: Sequence Learning, exam, 90 min |
Letzte Änderung: 11.4.2024 |
für die Ukraine